From AI Tools to Clinical Stability Integrating System Dynamics into AI Implementation Frameworks (SALIENT × URM)

Abstract

This paper examines why many artificial intelligence (AI) systems fail to achieve sustained clinical impact despite strong technical performance. Using the SALIENT AI implementation framework as a reference architecture, it argues that a key limitation lies in the static representation of disease within most AI systems. The paper introduces the Universal Resonance Model (URM) as a complementary system-dynamic layer that models disease as a process of instability, recovery, and phase transition rather than a fixed state. By integrating URM into AI implementation workflows, the paper outlines how AI systems can support clinicians by detecting loss of physiological stability and timing-sensitive intervention windows, rather than focusing solely on outcome prediction. The work is conceptual and framework-level, intended to support safer, more clinically aligned AI deployment across complex, chronic disease contexts.

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